1 Motivation

The primary purpose of the statistical system is to help users of statistics make better decisions or to hold those decision makers accountable. In the words of Principle 1 of the Fundamental Principles of Official Statistics, the statistics must “meet the test of practical utility”, serving “the Government, the economy and the public with data about the economic, demographic, social and environmental situation.” The national statistical system, or NSS, plays a crucial role in modern economies. It provides stakeholders, ranging from policy makers to stock market analysts and the general public, with the latest data on the country’s socio-economic developments. At the international level, monitoring progress on global undertakings such as the recently established Sustainable Development Goals (SDGs) requires high-quality data that must be produced consistently across different national statistical systems. Assessing and improving the capacity of a country’s NSS has long been a part of the global agenda. Since the early 2000s, a few capacity assessment tools have been developed to identify the weaknesses and strengths of national statistical systems by other organizations including PARIS21, the Food and Agriculture Organization of the United Nations (FAO), the United Nations Economic Commission for Europe (UNECE), the United Nations Economic Commission for Africa (UNECA), and the U.S. Census Bureau.

The World Bank’s Statistical Capacity Index (SCI) is one such tool that has been widely employed. Several international and national agencies have adopted the SCI for measuring progress in statistical capacity building and related investments. The World Bank mainstreamed the SCI in its monitoring and assessment framework and has adopted it as a baseline indicator in various projects at the country level. The SCI is based on publicly available data, and this has various advantages over other indexes of statistical capacity. A key advantage of the SCI is that it can provide assessment of a country’s statistical capacity in an internationally comparable and cost-effective manner.

Yet, there are several areas in which the existing SCI can be improved. First, it comprises of a limited number of indicators and includes no indicators of some important data sources, such as labor force surveys, establishment surveys, or administrative data. Second, it ignores the data dissemination practices of an NSS, which is one of the key features of data usage. Third, the SCI has been criticized for placing too much weight on statistical output and activities, while neglecting the infrastructure and resource components of statistical systems. Finally, it is silent on whether the data products produced by the NSS are in high demand. Since its launch in 2004, the SCI’s methodology and coverage have basically remained the same, while the global data landscape has changed significantly. NSSs have made significant advancements with data collection and dissemination practices. At the same time, the adoption of the Sustainable Development Goals (SDGs) set an ambitious development agenda for the next 15 years on ending poverty, protecting the planet, and ensuring prosperity for all by 2030. This, in turn, increased the demand for data and raised the bar for national statistical systems regarding their capacity to produce high-quality data. We thus propose to improve the current SCI to better suit the changing global data landscape.

2 Overview of the New SPI

The new Statistical Performance Indicators (SPI) builds on the SCI, which the World Bank has regularly published since 2004. Our new SPI will cover many of the same elements as the SCI, such as statistical methodology, source data, and periodicity, but will also expand into new areas. The goals are to offer a framework that was forward looking, measured less mature statistical systems as well as advanced systems, covered the entire national statistical system, not just the National Statistical Office (NSO), and gives countries incentives to build a modern statistical system. We also are committing to making our project open data and open code to build confidence in our work.

The new Statistical Performance Indicators (SPI) are designed to monitor how well countries statistical systems are meeting this purpose. By helping countries and development partners identify the strengths and weaknesses of national statistical systems the SPI can support policy advice for countries about their national statistical systems, investment decisions for donors including the World Bank, benchmarking of national statistical systems, and advocacy for national statistics.

We identify five key dimensions of a country’s statistical performance. These are data use, data services, data products, data sources, and data infrastructure . These dimensions can be presented in the form of a dashboard that can help countries identify areas for development in their statistical system. Improvements in performance can be represented as a virtuous data cycle that can become self-sustaining.

Statistics have no value unless they are used. The first dimension of the Dashboard is therefore data use. A successful statistical system is one that produces data products that are highly used.

In order to meet those needs, the statistical system needs to develop a range of services that connect users and suppliers and facilitate dialogue between them. The second dimension of the SPI is therefore data services that are trusted by users. A successful statistical system is one with highly valued and well used statistical services.

The dialogue between users and suppliers in turn drives the design of statistical products that are to be created including the quality of product needed for the country requirement. This will incorporate accuracy, timeliness, frequency, comparability and levels of disaggregation. The third dimension of the SPI is therefore data products. A successful statistical system is one that generates high quality statistical indicators that can track progress for the Sustainable Development Goals (SDGs).

In order to create the products required, the statistical system needs to make use of a variety of sources from both inside and outside the government. This will include making use of typical data collection methods like censuses and surveys, but also administrative data, geospatial data, and data generated from the private sector and from citizens. The fourth dimension of the dashboard is therefore data sources. A successful statistical system is one which draws on all types of data sources relevant to the indicators that are to be produced.

For the cycle to be complete, capability needs continuously to be reviewed to ensure that it is enough to deliver the products, services and ultimately data use required. The fifth dimension of the SPI is therefore data infrastructure. A successful statistical system is one that develops both hard infrastructure (legislation, governance, standards) and soft infrastructure (skills, partnerships) and has the financial resources to deliver. The 5 dimensions and associated 22 pillars of the SPI are as shown in Figure 1 below.

Figure 1: The Dimensions and Pillars that Construct the New SPI unchanged image

A score against each element would facilitate:

  1. Understanding of the maturity of the national statistical system in relation to others eg quintile groups of countries could be shown against each dimension
  2. This in turn would highlight relative strengths and weaknesses of the system and give an indication of the extent to which the official statistics could be relied upon
  3. It would also point to which other countries the country in question could learn from as it seeks to improve and create incentives to develop in a forward looking rather than backward looking way
  4. Time series would allow assessments to be made of progress of the system and a start point for assessments of return on investment for funding given for capacity building
  5. A dynamic view encouraging continuous improvement. As countries improve the bar for what good looks like would get higher

Key characteristics of the SPI are: (i) uses only publicly accessible data; (ii) transparent methodology; (iii) easily replicable; (iv) provides a long-time series to track progress in performance; (v) captures outcomes and supporting elements; (vi) reflects the SDGs; (vii) facilitates at-a-glance comparisons on a global scale. We are collecting data on indicators for the 22 pillars above. For dissemination, the SPI will be presented both in the dashboard format above and as an index for each country. Further details on the construction of the new SPI are provided in the remainder of the document.

3 Pillars of the new SPI

A quick primer on names. We refer to the 5 rows in the framework in Figure 1 as dimensions. We refer to the 22 cells in the framwork in Figure 1 as pillars. Finally, each pillar may be composed of multiple indicators. For instance, the pillar on censuses and surveys is made up indicators on whether population censuses have been conducted, agriculture censuses, labor force surveys, etc.

3.1 Data use

The data use dimension is segmented by user type. The tiles on the Dashboard provide an indicator of use of statistics respectively by the legislature, executive, civil society (including sub-national actors), academia and international bodies. A mature system would score well across the tiles. Areas for development would be highlighted by weaker scores in that domain enabling questions to be asked about prioritization amongst user groups and why existing services are not resulting in higher use of national statistics in that segment.

3.2 Data services

The data services dimension is segmented by service type. The tiles on the Dashboard provide an indicator of the quality of data releases, the richness and openness of online access, the effectiveness of advisory and analytical services related to statistics and the availability and use of data services such as secure microdata access. Advisory and analytical services might incorporate elements related to data stewardship services including ethical consideration of proposals and calling out misuse of data in accordance with the Fundamental Principles of Official Statistics.

3.3 Data products

The data products dimension is segmented by topic and organized into social, economic, environmental and institutional domains using the typology of the Sustainable Development Goals. This approach enables comparisons across countries and anchors the system in the 2030 agenda so that a global view can be generated whilst enabling different emphasis to be applied in different countries to reflect the user needs of that country.

3.4 Data sources

The data sources dimension is segmented between sources generated by the statistical office (censuses and surveys) and sources accessed from elsewhere (administrative data, geospatial data, private sector data and citizen generated data). The appropriate balance between these types of source will vary depending on the institutional setting and maturity of the statistical system in each country. High scores should reflect the extent to which the sources being utilized enable the necessary statistical indicators to be generated. For example, a low score on environment statistics may reflect a lack of use of (and low score for) geospatial data. This linkage, which is inherent in the data cycle approach, should help highlight areas for investment if country needs are to be met.

3.5 Data infrastructure

The data infrastructure dimension is segmented into hard and soft infrastructure segments itemizing essential cross cutting requirements for an effective statistical system. The segments are:

  1. Legislation and governance covering the existence of laws and a functioning institutional framework for the statistical system
  2. Standards and methods addressing compliance with recognized frameworks and concepts
  3. Skills including level of skills within the statistical system and amongst users (statistical literacy)
  4. Partnerships reflecting the need for the statistical system to be inclusive and coherent
  5. Finance, both domestically and from donors

3.6 Indicators and weights

We are seeking answers to the questions:

  1. What indicator (that we can measure in practice) could act as a proxy measure for the concept we are trying to capture?
  2. What weight can we assign to the indicator that allows this concept to be aggregated with others in a meaningful way?

There will be no right answer to these questions and the proposed approach makes a virtue of this by:

  1. Allowing missing values and setting out how missing values are dealt with for the purposes of making comparisons over time and between countries
  2. Providing users the opportunity to apply their own weights
  3. Releasing the dashboard as a “Beta” and encouraging a research agenda to help fill in gaps and improve any elements
  4. Establishing a process for review that enables time series to be respected.
  5. Presenting the dashboard within a toolkit for understanding statistical performance

Below is a brief description of the 22 pillars in our framework. A detailed description of the underlying indicators populating the pillars is also available in the annex.

SPI Pillars Metadata.

Pillar

Brief Description

Pillar 1.1: Data use by national legislature

Based on PARIS21 data use indicator (Chapter 4 of Statistical Capacity Development Outlook) using national
legislature website as source.

Pillar 1.2: Data use by national executive branch

Based on PARIS21 data use indicator (Chapter 4 of Statistical Capacity Development Outlook) using
voluntary national review plans as a source.

Pillar 1.3: Data use by civil society

Based on PARIS21 data use indicator (Chapter 4 of Statistical Capacity Development Outlook) using main social media platform in use in country as source.

Pillar 1.4: Data use by academia

Total number of downloads of the censuses and surveys produced by that country on a per capita basis according to IHSN and World Bank Microdata library.

Pillar 1.5: Data use by international organizations

Three measures usefulness or reliability of country produced measures for international organizations. First, on comparability of poverty estimates for the World Bank reporting on international poverty (Source: Povcalnet). Second on usable surveys for statistics on child mortality for the UN Inter-agency Group for Child Mortality Estimation (Source: https://childmortality.org/). Third on accuracy of debt reporting as classified by the World Bank (Source: World Bank WDI metadata).

Pillar 2.1: Data releases

IMF GDDS compliance indicator. 1 Point. Subscribing to IMF SDDS+ or SDDS standards. 0.5 Points. Subscribing to IMF e-GDDS standards. 0 Points. Otherwise. Source: IMF Dissemination Standards Bulletin Board.

Pillar 2.2: Online access

This indicator measures the richness and openness of online access. Source: Open Data Watch Openness score

Pillar 2.3: Advisory/ Analytical Services

New indicator of number of non-recurring products on NSO website (ad hoc/experimental rather than regular releases). The indicator is the number of products found. No established source exists for this indicator.

Pillar 2.4: Data services

NADA microdata cataloging available for surveys produced by NSO. NADA is an open source microdata cataloging system, compliant with the Data Documentation Initiative (DDI) and Dublin Cores RDF metadata standards. Source: NSO websites.

Pillar 3.1: social statistics

Fraction of Indicators in SDG goals 1-6 with value produced by countries statistical system. Source: UN Global SDG Indicators Database.

Pillar 3.2: economic statistics

Fraction of Indicators in SDG goals 7-12 with value produced by countries statistical system. Source: UN Global SDG Indicators Database.

Pillar 3.3: environmental statistics

Fraction of Indicators in SDG goals 13 & 15 with value produced by countries statistical system. Source: UN Global SDG Indicators Database.

Pillar 3.4: institutional statistics

Fraction of Indicators in SDG goals 16 & 17 with value produced by countries statistical system. Source: UN Global SDG Indicators Database.

Pillar 4.1: censuses and surveys

Availability of recent censuses and surveys covering broad areas. The following
censuses and surveys are considered: Population & Housing census, Agriculture census, Business/establishment census, Household Survey on income/ consumption/ expenditure/ budget/ Integrated Survey, Agriculture survey, Labor Force Survey, Health/Demographic survey, Business/establishment survey. Source: NSO websites, World Bank microdata library, ILO microdata library, IHSN microdata library

Pillar 4.2: administrative data

Indicator on quality of vital registration data plus score based on density of administrative data available on key World Bank, UNESCO and ILO databases

Pillar 4.3: geospatial data

This indicator measures the maturity of a countries geospatial data system by examining whether indicators accessible at sub-national levels. Source: Open Data Watch availability of key indicators at first administrative level score.

Pillar 4.4: Private/citizen generated data

New indicator based on references to private/citizen generated data in metadata relating to content on NSO website. No established source for pillar.

Pillar 5.1: Legislation and governance

New indicator based on PARIS21 indicators on SDG 17.18.2 (national statistical legislation compliance with UN Fundamental Principles of Official Statistics), existence of National Statistical Council, national statistical strategy generation, national statistical plan. Also include some other legislative aspects that foster good use of statistics e.g. freedom of information, privacy/transparency, good governance (e.g. free and fair elections).

Pillar 5.2: Standards and Methods

Internationally accepted and recommended methodology, classifications and standards provide the basis for national statistical offices (NSOs) on data integration, facilitating data exchange and providing the foundation for the preparation of relevant statistical indicators. The following methods and standards are considered: System of national accounts in use, National Accounts base year, Classification of national industry, CPI base year, Classification of household consumption, Classification of status of employment, Central government accounting status, Compilation of government finance statistics, Compilation of monetary and financial statistics, Business process. Source:

Pillar 5.3: Skills

New indicator drawing on PARIS21 indicators such as statistical society presence and data literacy.

Pillar 5.4: Partnerships

New indicator based on textual analysis of NSS reports/websites for references to partner organizations. No established source.

Pillar 5.5: Finance

Indicator based on PARIS21 SDG indicators (SDG 17.18.3 (national statistical plan that is fully funded and under implementation) and SDG 17.19.1 (value of resources made available to strengthen statistical capacity)). Could also incorporate indicator of NSO budget as a percentage of GDP.

4 Index Methodology

For research purposes, we create an index combining several indicators together to get an overview of country performance. The Index will contain only a subset of the pillars from our framework. For pillars excluded, we either lacked a source with a developed methodology or else the data collection for that measure was incomplete. This is described below:

This process of eliminating some pillars due to lack of established methodology or country coverage results in 12 pillars. This includes 1 on data use, 3 on data services, 4 on data products, 3 on data sources, and 1 on data infrastructure.

4.1 Overall Score

Our statistical performance indicators have a three level structure, and our overall score will be formed by sequentially aggregating each level.

To begin we produce a score for each pillar, which is an unweighted average of the indicators within that pillar. For instance, the Census and Surveys pillar will be formed by taking the unweighted average of the Population Census score, the Agriculture Census score, the Business Census score, the Labor Force Survey score, the Health Survey score, etc.

\[ SPI.PILLAR{ctds} = \sum_{i=1}^{N_I} \frac{SPI.IND_{ctdsi}}{N_I} \]

where \(SPI.PILLAR{ctds}\) is pillar s, in dimension d, in time period t, and country c. \(SPI.IND_{ctdsi}\) is an indicator (e.g. population census score).

After computing a score for the pillar, we then compute a dimension score, which is the average of the pillars in that dimension. For dimensions 1, 2, 4, and 5, we take the unweighted average of the pillars in the dimension. However, for Dimension 3 on data products, we take a weighted average of the pillars, where the weights are based on the number of SDGs in each pillar (6 SDGs in Pillar 3.1 on social statistics, 6 SDGs in Pillar 3.2 on economic statistics, 2 in Pillar 3.3 on environmental staistics, and 2 in Pillar 3.4 on institutional statistics). We take the perspective that all SDGs are of equal importance, and therefore weight our pillars accordingly.

\[ SPI.DIM_{ctd} = \sum_{s=1}^{N_S} \frac{\omega_{ds} \times SPI.PILLAR{ctds}}{N_S} \]

\(\omega_{ds}\) is the weight for pillar s in dimension d.

After calculating the scores for each dimension The SPI index is the average across the 5 dimensions.

The SPI index is scaled to have a maximum score of 100 and a minimum of 0. A score of 100 would indicate that a country has every single element that we measure in place. A score of 0 indicates that none are in place. To be precise:

\[ SPI.INDEX_{ct} = \sum_{d=1}^{N_D} \frac{SPI.DIM_{ctd}}{N_D} \]

Where SPI.INDEX is the SPI overall index. SPI.DIM are the 5 SPI dimensions listed above. In the notation, c is a country, t is the date, d is a dimension.

The nested structure of our index and the summation methods used to build an overall score ensure the axiomatic properties outlined in (Cameron et al. 2019). These include symmetry, monotonicity, and subgroup decomposability.

4.2 Handling Missing Data

For a full description of the methodology behind each specific indicator, please consult the technical documentation. However, we did follow an approach of handling missing values and lining up data, which I will describe in general terms below. For indicators where we had a value for a previous year (say a value in 2018 but not 2019), we would fill in from the previous value. For instance, the open data watch indicators on geo-spatial data was only released in 2018, not 2019, so we filled in the value for 2019 with the value from 2018 as our best estimate of that indicator.

For indicators where we had no data for any years, we chose not to impute a value. In that case, the value for that indicator is null and the country will not have a value for any pillars or dimensions where that value is used.

4.3 Process for Disputing the Data

Countries will be given an opportunity to dispute the values that make up our indicators. Our team takes every effort to make sure the data presented in ouf Statistical Performance Indicators are accurate, but it is possible that the sources we used to assign values for our indicators are not accurate despite these efforts. Because of this, countries will have a window to provide documentation for any disputed values, and our team will be happy to adjust our scores based on this new information. Precise details for this process will be made available in the future.

5 Analysis

Below we show a set of summary measures for our SPI index. We begin by presenting a world map containing the SPI index values for 2019 for each country. In total, we have 166 countries with sufficient data to compute an index value. This set of countries covers 98.6 percent of the world population.

The map is color coded based on the performance of countries on our index. Given the imprecision inherent in the calculations we recommend that the color coding provides the most detailed subdivisions of maturity. Finer distinctions are unlikely to provide meaningful differentiation between countries.

Countries shaded in dark red are the lowest performing, countries in dark green are the highest performing. Countries are grouped into three groups:

 

Figure 5.1:  

 

Figure 5.2:  

 

Figure 5.3:  

 

Figure 5.4:  

 

Figure 5.5:  

5.1 Unique Values

As a check of the data, we calculate the number of unique scores for our SPI index. If our SPI index produces a large number of tied scores, for instance, then our index will be less able to distinguish between the statistical performance of countries. When calculating the number of unique values for 2019, we find that there are 165 unique scores for 166 countries. This means there are 1 tied values. When looking within each dimension.

When looking at each specific dimension, there are only 12 unique scores for Dimension 1 on data use. The data use indicator is coming solely from dimension 1.5 on data use by international organizations. For Dimension 2, there are 158 unique scores. For Dimension 3, there are 164 unique scores. There are 159 unique scores for dimension 4, and there are 19 unique scores for dimension 5.

5.2 Relationship to GDP Per Capita and the Human Capital Index

Below, we will present the correlation between our SPI index and the log of GDP per capita and the World Bank’s Human Capital Index ((Bank 2020)). The source for GDP per capita comes from the World Bank’s World Development Indicators (WDI) database (NY.GDP.PCAP.KD). The GDP per capita numbers are in constant 2010 US$.

We would expect a strong positive relationship between GDP per capita of countries and their statistical system, as higher income countries would tend to have more resources available for statistical production. In fact, there is a strong relationship between the two. The correlation in 2019 between log GDP per capita and the SPI index is 0.74.1

Another measure of a country’s development is the Human Capital Index (HCI) developed by the World Bank ((Bank 2020)). The Human Capital Index is designed to capture the amount of human capital a child born today can expect to attain by age 18 in a country. The index combines a country’s child mortality, learning adjusted years of schooling, adult survival rates & stunting into one index. For more details, visit the Human Capital Index website (https://www.worldbank.org/en/publication/human-capital). Again, we would expect a strong positive relationship between a country’s HCI value and their Statistical Performance Indicators index value, as countries with a more developed human capital stock are likely to have greater capacity to produce statistics. Again, this is what we see. The correlation between the 2018 value of the HCI (the latest value available at the time of this writing) and the 2018 value of the SPI index is 0.86.

Below we show the scatter plot of the relationship between log GDP per capita, the HCI, and our SPI index for the years 2016-2019. In general, countries with higher per capita income and higher levels of human capital tend to have better performing statistical systems according to our measure.

 

(#fig:gdp_plot) 

So as to highlight countries where this relationships do not hold as well, the next figure shows the 15 countries that most over-perform and the 15 countries that most under-perform on the SPI Index compared to their levels of GDP per capita and the Human Capital Index. We would not necessarily expect a perfect fit between our SPI index and GDP per capita and the Human Capital Index, as countries differ in the resources put into their statistical system, even conditional on their levels of development. Highlighting outliers can sometimes be a useful exercise for determining whether a measure is identifying on the ground realities.

In order to produce this figure, we estimate an OLS regression of the SPI index in 2019 on log GDP per capita. We then calculate the residual, which can be interpreted as the difference between the country’s SPI index value and the expected index value based on their GDP per capita. Countries with values of the residual greater than zero are over-performing based on their GDP per capita and countries with residuals less than zero are under-performing. The corresponding figure for the Human Capital Index is calculated similarly, however in this case we do not take the log, as we did with GDP per capita.

 

Figure 5.6:  

5.3 Country Changes in the Index

In order to assess how stable our index values are over time, next we will compare the index values in 2016 to the 2019 values. Overall, the SPI index is quite stable over time. The correlation between the 2016 value and the 2019 value is 0.97.

 

(#fig:changes_plot) 

While the scores were relatively stable over time, some countries did see large improvements in their score from 2016-2019. The country that improved most on our index from 2016 to 2019 was Myanmar. Myanmar improved by 22.8 points on our scale out of 100. The table below shows the changes in our SPI index for the top 10 largest improvers.

Top 10 Countries with Largest Changes from 2016-2019.

Country

SPI Index Score 2019

SPI Index Score 2016

Difference

Myanmar

52.6

29.8

22.8

Saudi Arabia

47.1

29.8

17.2

Uzbekistan

52.2

35.7

16.4

Panama

55.5

39.2

16.3

Azerbaijan

60.8

47.1

13.7

Lebanon

45.1

31.7

13.4

Djibouti

32.8

19.6

13.2

Mauritania

37.9

25.7

12.2

Algeria

43.5

31.7

11.7

Belize

47.1

35.7

11.4

5.4 Comparison to the Statistical Capacity Indicators

Next, we compare our index to several other indices of statistical performance that have been created. This will provide a sense of how rankings differ across measures, how they correlate with other outcomes, and how the distributions of scores compare.

We compare the SPI index to the older World Bank Statistical Capacity Indicators (http://datatopics.worldbank.org/statisticalcapacity/SCIdashboard.aspx). The correlation between the SPI index and the SCI is 0.754.

 

Figure 5.7:  

Next, we show how the relationship to Log GDP per capita differs for the SCI and the SPI index using linear regression. We also include the Open Data Watch and the version of the Statistical Performance Index developed in (Cameron et al. 2019). While showing a strong relationship between an index and log GDP per capita does not mean the index is necessarily correct, and is certainly not necessarily causal, it does provide a face validity check of the index. Heteroskedasticity robust standard errors are shown in the table.

Overall, the new SPI index has the strongest relationship to GDP per capita. The linear regression estimates indicate that a 1% increase in GDP per capita is associated with a 0.1 point increase in our SPI index score. The r-squared from this regression is 0.49.

(#tab:sci_spi_reg_tab)Linear Regressions of Alternate Statistical Performance Indices on Log GDP per Capita

SPI Index

SCI

Open Data Watch

SPI Version 0

Log GDP per Capita

9.95 ***

2.96 ***

7.34 ***

3.95 ***

(0.30)

(0.58)

(0.34)

(0.77)

Year: 2017

1.20

-1.04

0.53

-0.06

(1.73)

(1.87)

(1.43)

(1.90)

Year: 2018

2.31

-2.44

4.69 **

4.53 *

(1.69)

(1.83)

(1.48)

(1.95)

Year: 2019

2.83

-2.01

5.10 ***

(1.65)

(1.84)

(1.47)

Intercept

-31.28 ***

43.81 ***

-22.45 ***

22.13 ***

(2.83)

(4.67)

(2.99)

(6.06)

N

626

563

682

416

R2

0.49

0.04

0.40

0.08

*** p < 0.001; ** p < 0.01; * p < 0.05. Heteroskedasticity Robust Std Errors in Parenthesis

&nbsp;

(#fig:sci_spi_reg_tab) 

5.5 ODIN ranking comparison

We compare the SPI index to the Open Data Watch rankings of country statistical systems (https://odin.opendatawatch.com/report/rankings). The correlation between the SPI index and the ODIN index is 0.845

&nbsp;

Figure 5.8:  

5.6 Comparison to Version 0 of the SPI

As a final comparison, we compare our new SPI index to an index developed by (Cameron et al. 2019), which can be thought of as a version 0 of our SPI index. The authors use similar data sources for their index. However, there are some differences.

First, the similarities. The methodology for constructing the index is the same. Also, the censuses and surveys (Indicator 4.1) and standards and methods (Indicator 5.2) are identical. The indicator for data releases (Indicator 2.1) and data services (Indicator 2.4) are pulled from the information collected in the fourth dimension on dissemination practices from version 0 of the SPI in Cameron et al. (2019). Finally, both indicators include an indicator for Complete Vital Registration Statistics (CRVS). The CRVS indicator is in the administrative data section of the new SPI index, while it was in the standards, methods, and classifications section of the SPI version 0.

For the differences, SPI version 0 had four dimensions, namely: (i) Methodology, Standards and Classifications (MSC), which provides information on the technology being used by the NSS; (ii) Census and Surveys (CS), which describes the intermediate products of the NSS; (iii) Availability of Key Indicators (AKI), which focuses on key final products needed for policy; and (iv) Dissemination Practices and Openness (DPO), which evaluates the extent to which products are publicly disseminated.

The indicator on AKI, from version 0, is conceptually similar to our Data Products dimension in the new SPI, but uses different sources of data and the DPO section is similar to our data services section, but draw on some different sources in some cases.

We compare the SPI index to the SPI version 0. The correlation between the SPI index and the version 0 index is 0.908

&nbsp;

(#fig:spi_v0) 

5.7 Density Plots

As another check, we present the distribution of scores across countries for each year and compare this distribution to a normal distribution. This exercise checks for whether the distribution of our SPI index scores contains significant skew or fat tails. There is some indication of a bunching of scores near the top of the distribution of SPI scores. This is due to a large number of OECD countries possessing similar scores on our index. This is not unexpected as OECD requires member countries to adhere to several methodological standards and to regularly report on a large set of indicators. These countries also are composed of several of the highest income countries that tend to be on the frontier of statistical production.

We also compare the distribution of our index to other measures of statistical performance. These are the SCI, the Open Data Watch index (ODIN), and version 0 of the SPI index that was produced in (Cameron et al. 2019)

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Figure 5.9:  

&nbsp;

Figure 5.10:  

6 Dimension Index Scores

Next, we present the scores for each dimension that compose the SPI index (Data Use, Data Services, Data Products, Data Sources, and Data Infrastructure). These figures can help show where (which dimension) variation is coming from across countries and to see where countries may be struggling in specific areas.

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(#fig:dimensions_plot-1) 

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(#fig:dimensions_plot-2) 

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(#fig:dimensions_plot-3) 

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(#fig:dimensions_plot-4) 

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(#fig:dimensions_plot-5) 

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(#fig:dimensions_plot-6) 

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(#fig:dimensions_plot-7) 

&nbsp;

(#fig:dimensions_plot-8) 

&nbsp;

(#fig:dimensions_plot-9) 

&nbsp;

(#fig:dimensions_plot-10) 

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(#fig:dimensions_plot-11) 

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(#fig:dimensions_plot-12) 

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(#fig:dimensions_plot-13) 

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(#fig:dimensions_plot-14) 

&nbsp;

(#fig:dimensions_plot-15) 

&nbsp;

(#fig:dimensions_plot-16) 

&nbsp;

(#fig:dimensions_plot-17) 

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(#fig:dimensions_plot-18) 

&nbsp;

(#fig:dimensions_plot-19) 

&nbsp;

(#fig:dimensions_plot-20) 

&nbsp;

(#fig:dimensions_plot-21) 

&nbsp;

(#fig:dimensions_plot-22) 

&nbsp;

(#fig:dimensions_plot-23) 

&nbsp;

(#fig:dimensions_plot-24) 

&nbsp;

(#fig:dimensions_plot-25) 

7 Annex

7.1 Country SPI Index values

Below, we present the full list of countries by their SPI Index Score in 2019. The first column is the country name and the following columns are the overall SPI index score, and then the sub-scores for dimension 1,2,3,4 and 5.

The cells in the table are color coded based on the performance of countries on our index and the sub-scores of the dimensions. Given the imprecision inherent in the calculations we recommend that the color coding provides the most detailed subdivisions of maturity. Finer distinctions are unlikely to provide meaningful differentiation between countries.

Countries shaded in dark red are the lowest performing, countries in dark green are the highest performing. Countries are grouped into three groups:

  • Low Performers: Countries in the bottom 33% are classified in this group. Shading is in red. Countries in bottom half of this group are shown in dark red.
  • Middle Performers: Countries in the middle 33% are classified in this group. Shading in yellow. Countries in the bottom half of this group are shown in light yellow.
  • High Performers: Countries in the top 33% are classified in this group. Shading in green. Countries in the bottom half of this group are shown in light green.

Overall SPI Index in 2019 and Dimension Scores.

Country

SPI Index Value

Dim 1: Data Use

Dim 2: Data Services

Dim 3: Data Products

Dim 4: Data Sources

Dim 5: Data Infrastructure

Finland

86.8

100.0

94.9

58.1

80.7

100

Norway

86.4

100.0

92.2

54.2

85.3

100

United States

85.7

100.0

94.0

51.3

83.3

100

Canada

85.7

100.0

93.2

45.2

89.9

100

Poland

85.6

100.0

95.1

46.8

86.3

100

Korea, Rep.

85.5

100.0

93.4

57.6

76.5

100

Japan

85.5

100.0

90.5

54.7

82.2

100

Australia

85.4

100.0

92.7

57.2

77.1

100

Czech Republic

85.2

100.0

90.1

55.0

80.8

100

Sweden

84.8

100.0

94.9

47.9

81.1

100

Austria

84.5

100.0

91.3

53.6

77.5

100

Spain

84.5

100.0

90.9

49.8

81.6

100

Italy

84.3

100.0

91.9

51.7

78.0

100

Netherlands

84.2

100.0

98.5

45.0

77.7

100

Switzerland

84.2

100.0

87.7

53.1

80.0

100

Germany

83.6

89.0

96.5

52.7

79.9

100

Slovak Republic

83.5

100.0

94.9

45.2

77.4

100

Mexico

83.4

100.0

92.9

71.2

77.7

75

Denmark

83.0

89.0

98.7

46.5

80.7

100

France

83.0

100.0

90.8

55.3

68.8

100

Slovenia

82.9

89.0

96.9

53.5

75.2

100

Lithuania

82.5

100.0

91.8

42.5

78.1

100

United Kingdom

81.6

100.0

86.8

52.6

68.8

100

Portugal

81.6

100.0

90.8

47.4

70.0

100

Ireland

81.1

89.0

94.7

51.2

70.7

100

Chile

81.0

89.0

77.0

63.0

76.0

100

Greece

80.9

100.0

87.5

44.9

72.1

100

Turkey

80.7

100.0

84.0

63.9

55.4

100

Hungary

80.5

100.0

86.9

44.6

71.0

100

Singapore

80.5

100.0

100.0

35.7

66.7

100

Latvia

80.3

100.0

89.2

41.8

70.7

100

Bulgaria

79.6

100.0

90.1

55.8

61.9

90

Armenia

79.1

100.0

85.0

68.6

56.9

85

Estonia

78.9

77.7

93.9

49.0

74.2

100

Israel

78.6

100.0

85.7

38.5

68.7

100

Belgium

78.3

89.0

83.9

46.3

72.3

100

Moldova

77.8

100.0

94.2

46.2

58.6

90

Kyrgyz Republic

77.6

100.0

81.5

65.0

56.6

85

New Zealand

77.4

77.7

91.6

49.1

68.7

100

Georgia

77.4

100.0

86.5

62.1

58.4

80

Brazil

77.3

100.0

83.3

63.0

65.2

75

Romania

76.8

100.0

87.2

48.7

68.2

80

Costa Rica

76.1

89.0

86.5

70.5

64.7

70

Luxembourg

75.5

77.7

90.4

41.3

68.0

100

Belarus

75.0

100.0

79.2

58.3

52.6

85

Iceland

73.1

66.7

87.8

44.1

66.7

100

Russian Federation

72.8

78.0

83.7

50.4

61.8

90

Serbia

72.3

100.0

67.4

56.0

58.2

80

Albania

72.1

100.0

69.8

58.4

57.1

75

Ukraine

72.0

100.0

46.1

62.7

51.2

100

Kazakhstan

70.1

100.0

82.1

66.3

31.9

70

North Macedonia

67.9

89.0

88.1

43.2

54.3

65

Croatia

67.9

100.0

50.8

43.8

54.9

90

Thailand

67.5

100.0

76.5

62.1

29.0

70

Mauritius

66.7

66.7

86.2

52.2

58.2

70

Philippines

66.5

77.7

88.3

59.9

71.9

35

Mongolia

66.4

66.7

80.0

65.5

59.7

60

Indonesia

65.0

89.0

87.3

60.7

32.8

55

Egypt, Arab Rep.

64.4

66.7

76.8

60.5

58.2

60

Peru

63.9

89.0

85.8

66.6

38.3

40

Ecuador

63.7

89.0

87.7

63.1

33.6

45

Uruguay

63.4

100.0

83.9

52.2

60.8

20

Colombia

62.6

66.7

84.3

72.9

34.4

55

El Salvador

62.3

66.7

79.9

57.8

47.2

60

Montenegro

61.1

77.7

64.3

51.9

56.5

55

Azerbaijan

60.8

55.7

64.6

53.0

50.9

80

Argentina

60.6

66.7

79.5

66.1

55.8

35

India

60.0

66.7

88.0

49.9

40.5

55

Vietnam

59.8

89.0

64.0

44.9

66.0

35

Sri Lanka

59.7

55.7

81.0

57.3

59.6

45

Malaysia

58.8

33.3

84.0

64.5

61.9

50

South Africa

57.5

44.3

85.5

61.4

31.5

65

Bosnia and Herzegovina

57.4

66.7

60.7

51.9

47.6

60

China

56.8

89.0

46.4

53.8

29.5

65

Panama

55.5

66.7

62.8

57.9

60.1

30

Paraguay

54.3

66.7

63.1

60.0

26.8

55

Dominican Republic

53.1

66.7

65.6

50.8

22.5

60

Senegal

52.9

55.7

81.0

48.1

29.4

50

Iran, Islamic Rep.

52.7

66.7

59.8

44.7

57.1

35

Myanmar

52.6

66.7

67.1

60.3

33.9

35

Morocco

52.2

44.3

88.4

56.5

21.7

50

Uzbekistan

52.2

55.7

56.0

44.2

39.9

65

Uganda

52.0

55.7

64.2

53.5

21.6

65

Ghana

51.7

66.7

61.3

53.0

27.4

50

Honduras

50.2

66.7

62.4

57.9

24.0

40

Bolivia

50.2

77.7

68.5

56.5

28.3

20

Guatemala

49.9

33.3

62.1

62.7

56.3

35

Tunisia

49.8

66.7

85.7

47.4

19.2

30

Fiji

48.6

44.3

59.5

45.3

49.1

45

Jordan

48.6

44.3

80.8

58.7

29.3

30

West Bank and Gaza

47.4

0.0

86.6

46.7

43.7

60

Rwanda

47.2

33.3

72.4

49.2

36.0

45

Belize

47.1

66.7

62.3

46.1

30.3

30

Saudi Arabia

47.1

44.3

60.7

42.2

38.0

50

Bangladesh

46.8

66.7

61.6

56.4

24.2

25

Zimbabwe

46.2

44.3

59.9

49.9

31.9

45

Lebanon

45.1

33.3

62.6

49.1

50.5

30

Seychelles

45.0

44.3

46.5

39.5

44.9

50

St. Vincent and the Grenadines

44.9

44.3

62.7

38.8

43.5

35

Nicaragua

44.4

66.7

60.0

41.1

19.1

35

Bahrain

44.2

44.3

64.3

35.5

47.1

30

Maldives

44.1

44.3

29.9

45.5

50.9

50

Pakistan

44.1

44.3

61.0

54.2

30.8

30

Cambodia

44.0

33.3

59.2

55.1

27.4

45

Zambia

44.0

44.3

57.8

49.0

28.8

40

Algeria

43.5

55.7

60.1

44.5

17.1

40

Malawi

43.5

44.3

61.2

57.1

24.7

30

Nigeria

43.5

44.3

62.4

44.8

25.8

40

Cameroon

43.5

44.3

61.6

55.6

20.8

35

Kenya

43.4

33.3

60.5

53.1

25.2

45

Oman

43.3

66.7

44.4

38.2

27.0

40

Togo

43.1

33.3

59.8

52.5

25.0

45

Cabo Verde

43.1

33.3

59.3

50.7

52.0

20

Tanzania

42.8

22.0

60.7

57.6

28.8

45

Benin

42.5

66.7

58.3

42.6

20.0

25

Niger

41.9

33.3

62.0

54.9

24.5

35

Samoa

41.9

33.3

63.5

43.5

24.2

45

Botswana

41.9

33.3

66.6

51.9

17.5

40

Afghanistan

41.6

44.3

59.9

54.0

14.8

35

Lesotho

41.0

33.3

63.3

41.6

27.0

40

Jamaica

41.0

33.3

73.7

46.8

21.3

30

Mozambique

41.0

33.3

57.6

51.8

22.2

40

Burkina Faso

40.9

33.3

59.6

57.6

18.9

35

Angola

40.7

33.3

57.3

55.9

17.0

40

Liberia

40.6

66.7

28.8

41.3

21.4

45

St. Lucia

40.4

33.3

55.9

41.5

46.2

25

Burundi

40.4

33.3

61.7

55.5

16.4

35

Lao PDR

40.4

44.3

56.6

43.5

27.5

30

Nepal

39.4

33.3

61.3

59.6

22.5

20

Madagascar

38.9

44.3

56.3

50.7

13.4

30

Tajikistan

38.6

44.3

31.4

49.7

17.7

50

Sierra Leone

38.6

44.3

65.2

43.7

19.7

20

Bhutan

38.5

33.3

59.2

55.0

29.7

15

Ethiopia

38.1

44.3

60.0

50.3

20.9

15

Mauritania

37.9

33.3

62.5

39.0

19.5

35

Suriname

37.5

0.0

62.4

38.5

51.7

35

Mali

37.2

22.0

63.1

45.4

25.6

30

Timor-Leste

37.0

0.0

58.2

46.1

25.5

55

Guinea

36.5

44.3

61.4

41.5

15.2

20

Papua New Guinea

34.7

44.3

55.3

39.4

14.5

20

Namibia

34.5

0.0

63.1

55.3

24.1

30

Trinidad and Tobago

34.4

33.3

27.3

31.1

40.2

40

Gambia, The

33.7

33.3

28.5

51.8

19.7

35

Guyana

33.0

33.3

57.4

38.9

10.3

25

Eswatini

33.0

33.3

22.4

55.9

13.3

40

Djibouti

32.8

33.3

60.4

35.7

9.8

25

Solomon Islands

32.6

33.3

60.4

39.5

9.8

20

Venezuela, RB

32.5

22.0

59.9

39.7

15.9

25

Vanuatu

31.7

33.3

59.1

37.4

13.7

15

Sudan

31.3

22.3

57.9

50.2

16.2

10

Congo, Rep.

28.4

33.3

27.9

39.4

21.3

20

Congo, Dem. Rep.

27.2

33.3

26.3

38.3

17.9

20

Iraq

27.0

11.0

25.0

48.3

15.8

35

Yemen, Rep.

25.7

33.3

25.5

34.2

15.3

20

Guinea-Bissau

25.6

33.3

27.3

35.1

12.3

20

Chad

25.3

11.0

51.5

39.0

9.9

15

Haiti

24.9

33.3

19.7

40.5

10.8

20

South Sudan

24.1

0.0

41.8

35.1

13.5

30

Gabon

24.1

33.3

23.7

35.4

7.9

20

Syrian Arab Republic

21.4

11.0

24.8

32.6

13.5

25

Kiribati

18.3

0.0

25.8

41.5

9.1

15

Micronesia, Fed. Sts.

17.3

0.0

25.6

32.2

13.5

15

Somalia

16.5

11.0

36.0

35.3

0.0

0

Libya

15.9

0.0

20.6

31.0

7.7

20

Marshall Islands

14.6

0.0

24.1

32.8

6.2

10

Turkmenistan

13.3

11.0

0.6

33.2

6.7

15

American Samoa

NA

0.0

NA

15.0

NA

NA

Andorra

NA

44.3

NA

22.7

NA

NA

Antigua and Barbuda

NA

33.3

NA

42.7

NA

25

Aruba

NA

33.3

NA

15.7

NA

NA

Bahamas, The

NA

44.3

60.6

30.9

NA

40

Barbados

NA

33.3

NA

40.8

NA

30

Bermuda

NA

33.3

NA

13.1

NA

NA

British Virgin Islands

NA

33.3

NA

15.1

NA

NA

Brunei Darussalam

NA

55.7

NA

32.9

NA

40

Cayman Islands

NA

33.3

NA

14.5

NA

NA

Central African Republic

NA

33.3

NA

36.5

NA

20

Channel Islands

NA

33.3

NA

NA

NA

NA

Comoros

NA

33.3

NA

42.6

NA

30

Cote d'Ivoire

NA

55.7

NA

49.6

NA

NA

Cuba

NA

33.3

NA

40.7

NA

NA

Curacao

NA

33.3

NA

15.6

NA

NA

Cyprus

NA

77.7

92.3

48.1

NA

85

Dominica

NA

44.3

NA

34.1

NA

35

Equatorial Guinea

NA

0.0

NA

36.5

NA

25

Eritrea

NA

11.0

NA

34.1

NA

10

Faroe Islands

NA

33.3

NA

9.0

NA

NA

French Polynesia

NA

33.3

NA

13.9

NA

NA

Gibraltar

NA

33.3

NA

9.5

NA

NA

Greenland

NA

33.3

NA

11.6

NA

NA

Grenada

NA

44.3

NA

39.7

NA

25

Guam

NA

33.3

NA

13.9

NA

NA

Hong Kong SAR, China

NA

33.3

NA

27.9

NA

NA

Isle of Man

NA

33.3

NA

7.9

NA

NA

Korea, Dem. People’s Rep.

NA

0.0

NA

35.6

NA

NA

Kosovo

NA

66.7

66.0

NA

20.4

60

Kosovo

NA

66.7

66.0

NA

20.4

60

Kuwait

NA

55.7

66.3

41.3

NA

30

Liechtenstein

NA

33.3

NA

20.3

NA

NA

Macao SAR, China

NA

33.3

NA

21.6

NA

NA

Malta

NA

77.7

84.0

38.9

NA

75

Monaco

NA

33.3

NA

19.7

NA

NA

Nauru

NA

0.0

NA

31.1

NA

5

New Caledonia

NA

33.3

NA

15.6

NA

NA

Northern Mariana Islands

NA

33.3

NA

9.9

NA

NA

Palau

NA

33.3

NA

32.7

NA

40

Puerto Rico

NA

33.3

NA

20.3

NA

NA

Qatar

NA

66.7

28.6

39.6

NA

25

San Marino

NA

33.3

NA

16.7

NA

55

Sao Tome and Principe

NA

33.3

NA

38.0

NA

NA

Sint Maarten (Dutch part)

NA

33.3

NA

8.4

NA

NA

St. Kitts and Nevis

NA

33.3

NA

24.4

NA

25

St. Martin (French part)

NA

33.3

NA

6.0

NA

NA

Taiwan, China

NA

33.3

NA

NA

NA

NA

Tonga

NA

33.3

NA

37.3

NA

35

Turks and Caicos Islands

NA

33.3

NA

11.7

NA

NA

Tuvalu

NA

0.0

NA

31.0

NA

5

United Arab Emirates

NA

66.7

36.9

33.1

NA

65

Virgin Islands (U.S.)

NA

33.3

NA

9.2

NA

NA

7.2 Indicator Metadata

SPI Indicator Metadata.

Indicator Name

Pillar

Brief Description

Scoring

Availability of Comparable Poverty headcount ratio at $1.90 a day (5 year moving average)

Pillar 1.5

Comparability data from World Bank's Povcalnet

1 Point. Comparable data lasting at least two years within past 5 years. 0 Points. No comparable data within past 5 years

Availability of Mortality rate, under-5 (per 1,000 live births) data meeting quality standards according to UN IGME (5 year moving average)

Pillar 1.5

Child Mortality Metadata from UN IGME

1 Point. At least three indicators that met UN IGME standards within past 5 years. 0.67 Points. Two indicators that met UN IGME standards within past 5 years. 0.33 Points. One indicators that met UN IGME standards within past 5 years 0 Points. None within past 5 years

Quality of Debt service data according to World Bank

Pillar 1.5

Debt Reporting Metadata from World Bank

1 Points. Actual value. 0.67 Points. Preliminary value 0.33 Points. Estimated value. 0 Points. No value

SDDS/e-GDDS subscription

Pillar 2.1

The Special Data Dissemination Standard (SDDS) and electronic General Data Dissemination Standard (e-GDDS) were established by the International Monetary Fund (IMF) for member countries that have or that might seek access to international capital markets, to guide them in providing their economic and financial data to the public. Although subscription is voluntary, the subscribing member needs to be committed to observing the standard and provide information about its data and data dissemination practices (metadata). The metadata are posted on the IMF’s SDDS and e-GDDS websites.

Point. Subscribing to IMF SDDS+ or SDDS standards 0.5 Points. Subscribing to IMF e-GDDS standards. 0 Points. Otherwise

ODIN Open Data Openness score

Pillar 2.2

ODW Openness score

Our source for this indicator is Open Data Watch. Scores range from 0-100. For more details, consult the ODIN technical documentation.

NSO Website

Pillar 2.3

New indicator of number of non-recurring products on NSO website (ad hoc/experimental rather than regular releases). The indicator is the number of products found.

No established methodology.

NADA metadata

Pillar 2.4

NADA/NSO websites. Statistical systems must be open and transparent about their methods and procedures and provide access to adequate metadata – detailed descriptions of the methods and procedures used to produce the data.

1 Point. Yes, available. 0 Points. No

GOAL 1: No Poverty (5 year moving average)

Pillar 3.1

SDG Goal 1 data availability. Source: UN Global SDG Indicators Database

Fraction of Indicators in Goal 1 with value produced by countries statistical system

GOAL 2: Zero Hunger (5 year moving average)

Pillar 3.2

SDG Goal 2 data availability. Source: UN Global SDG Indicators Database

Fraction of Indicators in Goal 2 with value produced by countries statistical system

GOAL 3: Good Health and Well-being (5 year moving average)

Pillar 3.3

SDG Goal 3 data availability. Source: UN Global SDG Indicators Database

Fraction of Indicators in Goal 3 with value produced by countries statistical system

GOAL 4: Quality Education (5 year moving average)

Pillar 3.4

SDG Goal 4 data availability. Source: UN Global SDG Indicators Database

Fraction of Indicators in Goal 4 with value produced by countries statistical system

GOAL 5: Gender Equality (5 year moving average)

Pillar 3.5

SDG Goal 5 data availability. Source: UN Global SDG Indicators Database

Fraction of Indicators in Goal 5 with value produced by countries statistical system

GOAL 6: Clean Water and Sanitation (5 year moving average)

Pillar 3.6

SDG Goal 6 data availability. Source: UN Global SDG Indicators Database

Fraction of Indicators in Goal 6 with value produced by countries statistical system

GOAL 7: Affordable and Clean Energy (5 year moving average)

Pillar 3.7

SDG Goal 7 data availability. Source: UN Global SDG Indicators Database

Fraction of Indicators in Goal 7 with value produced by countries statistical system

GOAL 8: Decent Work and Economic Growth (5 year moving average)

Pillar 3.8

SDG Goal 8 data availability. Source: UN Global SDG Indicators Database

Fraction of Indicators in Goal 8 with value produced by countries statistical system

GOAL 9: Industry, Innovation and Infrastructure (5 year moving average)

Pillar 3.9

SDG Goal 9 data availability. Source: UN Global SDG Indicators Database

Fraction of Indicators in Goal 9 with value produced by countries statistical system

GOAL 10: Reduced Inequality (5 year moving average)

Pillar 3.10

SDG Goal 10 data availability. Source: UN Global SDG Indicators Database

Fraction of Indicators in Goal 10 with value produced by countries statistical system

GOAL 11: Sustainable Cities and Communities (5 year moving average)

Pillar 3.11

SDG Goal 11 data availability. Source: UN Global SDG Indicators Database

Fraction of Indicators in Goal 11 with value produced by countries statistical system

GOAL 12: Responsible Consumption and Production (5 year moving average)

Pillar 3.12

SDG Goal 12 data availability. Source: UN Global SDG Indicators Database

Fraction of Indicators in Goal 12 with value produced by countries statistical system

GOAL 13: Climate Action (5 year moving average)

Pillar 3.13

SDG Goal 13 data availability. Source: UN Global SDG Indicators Database

Fraction of Indicators in Goal 13 with value produced by countries statistical system

GOAL 14: Life Below Water (5 year moving average)

Pillar 3.14

SDG Goal 14 data availability. Source: UN Global SDG Indicators Database

Fraction of Indicators in Goal 14 with value produced by countries statistical system

GOAL 15: Life on Land (5 year moving average)

Pillar 3.15

SDG Goal 15 data availability. Source: UN Global SDG Indicators Database

Fraction of Indicators in Goal 15 with value produced by countries statistical system

GOAL 16: Peace and Justice Strong Institutions (5 year moving average)

Pillar 3.16

SDG Goal 16 data availability. Source: UN Global SDG Indicators Database

Fraction of Indicators in Goal 16 with value produced by countries statistical system

GOAL 17: Partnerships to achieve the Goal (5 year moving average)

Pillar 3.17

SDG Goal 17 data availability. Source: UN Global SDG Indicators Database

Fraction of Indicators in Goal 17 with value produced by countries statistical system

Population & Housing census (Availability score over 20 years)

Pillar 4.1

Population censuses collect data on the size, distribution and composition of population and information on a broad range of social and economic characteristics of the population. It also provides sampling frames for household and other surveys. Housing censuses provide information on the supply of housing units, the structural characteristics and facilities, and health and the development of normal family living conditions. Data obtained as part of the population census, including data on homeless persons, are often used in the presentation and analysis of the results of the housing census. It is recommended that population and housing censuses be conducted at least every 10 years.

1 Point. Population census done within last 10 years. 0.5 Points. Population census done within last 20 years. 0 Points. Otherwise

Agriculture census (Availability score over 20 years)

Pillar 4.1

Agriculture censuses collect information on agricultural activities, such as size of holding, land tenure, land use, employment and production, and provide basic structural data and sampling frames for agricultural surveys. Censuses of agriculture normally involves collecting key structural data by complete enumeration of all agricultural holdings, in combination with more detailed structural data using sampling methods. It is recommended that agricultural censuses be conducted at least every 10 years.

1 Point. census done within last 10 years. 0.5 Points. census done within last 20 years. 0 Points. Otherwise

Business/establishment census (Availability score over 20 years)

Pillar 4.1

Business/establishment censuses provide valuable information on all economic activities, number of employed and size of establishments in the economy. Business Register information is establishment-based and includes business location, organization type (e.g. subsidiary or parent), industry classification, and operating data (e.g., receipts and employment).

1 Point. census done within last 10 years. 0.5 Points. census done within last 20 years. 0 Points. Otherwise

Household Survey on income, etc (Availability score over 10 years)

Pillar 4.1

These surveys collect data on household income (including income in kind), consumption and expenditure. They typically include income, expenditure, and consumption surveys, household budget surveys, integrated surveys. It is recommended that surveys on income and expenditure be conducted at least every 3 to 5 years.

1 Point. 3 or more surveys done within past 10 years. 0.6 Points. 2 surveys done within past 10 years. 0.3 Points. 1 survey done within past 10 years. 0 Points. None within past 10 years

Agriculture survey (Availability score over 10 years)

Pillar 4.1

Agricultural surveys refer to surveys of agricultural holdings based on the sampling frames established by the agricultural census. These are surveys on agricultural land, production, crops and livestock, aquaculture, labor and cost, and time use. Some issues, such as gender and food security, are of interest to most agriculture surveys.

1 Point. 3 or more surveys done within past 10 years. 0.6 Points. 2 surveys done within past 10 years. 0.3 Points. 1 survey done within past 10 years. 0 Points. None within past 10 years

Labor Force Survey (Availability score over 10 years)

Pillar 4.1

Labor force survey is a standard household-based survey of work-related statistics at the national and sub-national employment or unemployment levels, rates or trends. The surveys also provide the characteristics of the employed or unemployed, including labor force status by age or gender, breakdowns between employees and the self-employed, public versus private sector employment, multiple job-holding, hiring, job creation, and duration of unemployment.

1 Point. 3 or more surveys done within past 10 years. 0.6 Points. 2 surveys done within past 10 years. 0.3 Points. 1 survey done within past 10 years. 0 Points. None within past 10 years

Health/Demographic survey (Availability score over 10 years)

Pillar 4.1

Health surveys collect information on various aspects of health of populations, such as health expenditure, access, utilization, and outcomes. They typically include Demographic and Health Surveys. It is recommended that health surveys be conducted at least every 3 to 5 years.

1 Point. 3 or more surveys done within past 10 years. 0.6 Points. 2 surveys done within past 10 years. 0.3 Points. 1 survey done within past 10 years. 0 Points. None within past 10 years

Business/establishment survey (Availability score over 10 years)

Pillar 4.1

The business/establishment survey provides information on employment, hours, and earnings of employees from a sample of business establishments including private and public, entities that are classified based on an establishment's principal activity from the business or establishment census. Establishment surveys include surveys of businesses, farms, and institutions. They may ask for information about the establishment itself and/or employee characteristics and demographics.

1 Point. 3 or more surveys done within past 10 years. 0.6 Points. 2 surveys done within past 10 years. 0.3 Points. 1 survey done within past 10 years. 0 Points. None within past 10 years

Social Protection Admin (ASPIRE) (5 year moving average)

Pillar 4.2

Administrative data available on social protection programs from ASPIRE (World Bank) databases

Scoring is 1 if administrative data is available to produce beneficiary counts or expenditures for any social protection and labor program, 0 otherwise. In order to smooth out gaps in reporting, we take a moving 5 year average of the score.

Education (UNESCO) (5 year moving average)

Pillar 4.2

Administrative of nationally representative learning assessment

In order to score this indicator, we calculate the fraction of the indicators that were produced in a country in a year. In order to smooth out variation from year to year in reporting or conducting national assessments, we take a moving 5 year average.

CRVS (WDI)

Pillar 4.2

Civil Registration and Vital Statistics (CRVS) complete

Birth registrations 90% complete and death registration 75% complete according to UNSD.

Labor Admin (ILO) (5 year moving average)

Pillar 4.2

Administrative data available for labor statistics from ILO databases

To produce a score, we calculate the percentage of six admin sources that are available for a country in a given year. In order to smooth out gaps in reporting, we take a moving 5 year average of the score.

Geospatial data available at 1st Admin Level

Pillar 4.3

Indicator data availability at sub-national levels

Our source for this indicator is Open Data Watch. Indicator is whether data available at first administrative data level. Scores range from 0-100. For more details, consult the ODIN technical documentation.

NSO Website

Pillar 4.4

New indicator based on references to private/citizen generated data in metadata relating to content on NSO website

Legislation Indicator based on PARIS21 indicators on SDG 17.18.2

Pillar 5.1

Based on PARIS21 indicators on SDG 17.18.2 (national statistical legislation compliance with UN Fundamental Principles of Official Statistics), existence of National Statistical Council, national statistical strategy generation, national statistical plan. Also include some other legislative aspects that foster good use of statistics eg freedom of information, privacy/transparency, good governance (eg free and fair elections).

Scores is 1 if the country has a national statistical legislation compliant with United Nations Fundamental Principles of Statistics. Scores of 0 or scores with missing values are treated the same (both given a score of zero).

System of national accounts in use

Pillar 5.2

The national accounts data are compiled using the concepts, definitions, framework, and methodology of the System of National Account 2008 (SNA2008) or European System of National and Regional Accounts (ESA 2010). The manual has evolved to meet the changing economic structure, to follow systematic accounting and ensure international compatibility.

Scoring: 1 point for using SNA2008 or ESA 2010, 0.5 points for using SNA 1993 or ESA 1995. 0 points otherwise

National Accounts base year

Pillar 5.2

National accounts base year is the year used as the base period for constant price calculations in the country’s national accounts. It is recommended that the base year of constant price estimates be changed periodically to reflect changes in economic structure and relative prices.

1 point for chained price, 0.5 for reference period within past 10 years. 0 points otherwise.

Classification of national industry

Pillar 5.2

The industrial production data are compiled using the International Standard Industrial Classification of All Economic Activities (ISIC) Rev.4 and Statistical Classification of Economic Activities in the European Community (NACE) Rev.2. ISIC Rev.4 is a standard classification of economic activities arranged so that entities can be classified per the activity they carry out using criteria such as input, output and use of the products produced, more emphasis has been given to the character of the production process in defining and delineating ISIC classes for international comparability. The manual and classification have changed to cover the complete scope of industrial production, employment, and GDP and other statistical areas.

1 Point. Latest version is adopted (ISIC Rev 4, NACE Rev 2 or a compatible classification). 0.5 Points. Previous version is used (ISIC Rev 3, NACE Rev 1 or a compatible classification). 0 Points. Otherwise

CPI base year

Pillar 5.2

Consumer Price Index serves as indicators of inflation and reflects changes in the cost of acquiring a fixed basket of goods and services by the average consumer. Weights are usually derived from consumer expenditure surveys and the CPI base year refers to the year the weights were derived. It is recommended that the base year be changed periodically to reflect changes in expenditure structure.

1 Point. Annual chain linking. 0.5 Points. Base year in last 10 years. 0 points. Otherwise

Classification of household consumption

Pillar 5.2

Classification of Individual Consumption According to Purpose (COICOP) is used in household budget surveys, consumer price indices and international comparisons of gross domestic product (GDP) and its component expenditures. Although COICOP is not strictly linked to any particular model of consumer behavior, the classification is designed to broadly reflect differences in income elasticities. It is an integral part of the SNA1993 and more detailed subdivision of the classes provide comparability between countries and between statistics in these different areas.

1 Point. Follow Classification of Individual Consumption by Purpose (COICOP). 0 Points. Otherwise

Classification of status of employment

Pillar 5.2

Classification of status of employment refers to employment data that are compiled using the current international standard International Classification of Status in Employment (ISCE-93). It classifies jobs with respect to the type of explicit or implicit contract of employment between the job holder and the economic unit in which he or she is employed. Therefore, it aims to provide the basis for production of internationally comparable statistics on the employment relationship, including the distinction between salaried employment and self-employment.

1 Point. Follow International Labour Organization, International Classification of Status in Employment (ICSE-93) or 2012 North American Industry Classification System (NAICS). 0 Points Otherwise.

Central government accounting status

Pillar 5.2

Government finance accounting status refers to the accounting basis for reporting central government financial data. For many countries’ government finance data, have been consolidated into one set of accounts capturing all the central government’s fiscal activities and following noncash recording basis. Budgetary central government accounts do not necessarily include all central government units, the picture they provide of central government activities is usually incomplete.

1 Point. Consolidated central government accounting follows noncash recording basis. 0.5 Points. Consolidated central government accounting follows cash recording basis. 0 Points. Otherwise

Compilation of government finance statistics

Pillar 5.2

Compilation of government finance statistics refers to the Government Finance Statistics Manual (GFSM) in use for compiling the data. It provides guidelines on the institutional structure of governments and the presentation of fiscal data in a format similar to business accounting with a balance sheet and income statement plus guidelines on the treatment of exchange rate and other valuation adjustments. The latest manual GFSM2014 is harmonized with the SNA2008.

1 Point. Follow the latest Government Finance Statistical Manual (2014)/ ESA2010. 0.5 Points. Previous version is used (GFSM 2001). 0 Points. Otherwise

Compilation of monetary and financial statistics

Pillar 5.2

Compilation of monetary and financial statistics refers to the Monetary and Financial Statistics Manual (MFSM) in use. It covers concepts, definitions, classifications of financial instruments and sectors, and accounting rules, and provides a comprehensive analytic framework for monetary and financial planning and policy determination. The Monetary and Finance Statistics: Compilation Guide (2008) provides detailed guidelines for the compilation of monetary and financial statistics in addition to MFSM.

1 Point. Follow the latest Monetary and Finance Statistics Manual (2000) or Monetary and Finance Statistics: Compilation Guide (2008/2016). 0 Points. Otherwise

Business process

Pillar 5.2

The Generic Statistical Business Process Model (GSBPM) aims to describe statistics production in a general and process-oriented way. It is used both within and between statistical offices as a common basis for work with statistics production in different ways, such as quality, efficiency, standardization, and process-orientation. It is used for all types of surveys, and "business" is not related to "business statistics" but refers to the statistical office, simply expressed.

1 Point. GSBPM is in use. 0 Points. Otherwise

PARIS21 indicator on data literacy

Pillar 5.3

PARIS21 indicators such as statistical society presence and data literacy. PARIS21 may also have material on Internal skills capacity and numbers of statistically qualified people in the country.

The source is Paris 21. Score ranges from 0 to 100. Following the PARIS21 scoring, the score is the weighted sum of each of the four components’ scores, whose relative weight is reported in brackets: i) Basic Consideration (25%); ii) Diagnosis and quantification (24%); iii) Statistical Analysis (22%); iv) Disaggregation (29%).

Partnerships

Pillar 5.4

New indicator based on textual analysis of NSS reports/websites for references to partner organisations.

Finance Indicator based on PARIS21 indicators on SDG 17.18.3 & SDG 17.19.1

Pillar 5.5

Indicator based on PARIS21 SDG indicators (SDG 17.18.3 (national statistical plan that is fully funded and under implementation) and SDG 17.19.1 (value of resources made available to strengthen statistical capacity)). Could also incorporate indicator of NSO budget as a percentage of GDP.

Scores is 1 if the country has a national statistical plan that is fully funded and under implementation. Scores of 0 or scores with missing values are treated the same (both given a score of zero).

References

Bank, World. 2020. “The Human Capital Index 2020 Update : Human Capital in the Time of Covid-19.”

Cameron, Grant J, Hai-Anh H Dang, Mustafa Dinc, James Foster, and Michael M Lokshin. 2019. “Measuring the Statistical Capacity of Nations.”

  1. To understand what effect taking the log has on this correlation, the correlation in 2019 between (non-logged) GDP per capita and the SPI index is 0.64.↩︎